Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	51,354
2	South Dakota	46,040
3	Louisiana	39,735
4	Mississippi	39,183
5	Alabama	38,023
6	Iowa	37,670
7	Wisconsin	37,346
8	Florida	36,610
9	Tennessee	36,269
10	Arkansas	35,681
11	Georgia	34,424
12	Idaho	34,291
13	Nebraska	33,796
14	South Carolina	33,518
15	Utah	33,446
16	Arizona	32,994
17	Texas	31,860
18	Nevada	31,536
19	Illinois	30,704
20	Oklahoma	29,924
21	Rhode Island	29,682
22	Missouri	29,239
23	Kansas	27,721
24	Montana	27,528
25	New Jersey	26,245
26	New York	25,876
27	North Carolina	25,203
28	Indiana	25,102
29	Delaware	24,902
30	Minnesota	24,396
31	District of Columbia	23,954
32	Maryland	23,527
33	California	23,249
34	Kentucky	22,763
35	Massachusetts	22,203
36	New Mexico	20,587
37	Virginia	20,550
38	Wyoming	20,398
39	Alaska	20,093
40	Puerto Rico	19,881
41	Connecticut	19,251
42	Michigan	18,252
43	Ohio	17,344
44	Colorado	17,210
45	Pennsylvania	15,897
46	Washington	14,309
47	West Virginia	12,669
48	Hawaii	10,537
49	Oregon	10,152
50	New Hampshire	7,744
51	Maine	4,694
52	Vermont	3,386

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	North Dakota	986
2	South Dakota	975
3	Montana	708
4	Wisconsin	704
5	Wyoming	576
6	Alaska	574
7	Idaho	432
8	Utah	425
9	Rhode Island	417
10	Iowa	412
11	Nebraska	372
12	Tennessee	365
13	Illinois	343
14	New Mexico	338
15	Minnesota	319
16	Kentucky	313
17	Missouri	310
18	Indiana	302
19	Colorado	296
20	Kansas	295
21	Arkansas	260
22	Connecticut	241
23	Nevada	238
24	Puerto Rico	231
25	Michigan	230
26	Oklahoma	229
27	Alabama	214
28	South Carolina	209
29	Texas	204
30	Georgia	203
31	Ohio	197
32	North Carolina	187
33	West Virginia	184
34	Massachusetts	171
35	Mississippi	171
36	Pennsylvania	160
37	Florida	156
38	Louisiana	152
39	Arizona	149
40	New Jersey	149
41	Delaware	137
42	Maryland	131
43	Virginia	118
44	California	101
45	Washington	96
46	District of Columbia	94
47	Oregon	85
48	New York	82
49	New Hampshire	71
50	Hawaii	50
51	Maine	43
52	Vermont	37

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,835
2	New York	1,701
3	Massachusetts	1,434
4	Connecticut	1,288
5	Louisiana	1,263
6	Rhode Island	1,121
7	Mississippi	1,103
8	District of Columbia	912
9	Arizona	809
10	Illinois	778
11	Florida	768
12	Michigan	759
13	South Carolina	746
14	Georgia	721
15	Delaware	704
16	Pennsylvania	684
17	Maryland	679
18	North Dakota	631
19	Texas	626
20	Indiana	622
21	Arkansas	615
22	Alabama	589
23	Nevada	570
24	Iowa	532
25	Missouri	474
26	New Mexico	467
27	Tennessee	465
28	Ohio	448
29	California	442
30	Minnesota	429
31	South Dakota	423
32	Virginia	421
33	North Carolina	403
34	Colorado	393
35	New Hampshire	349
36	Kansas	336
37	Kentucky	334
38	Idaho	329
39	Wisconsin	325
40	Nebraska	322
41	Oklahoma	321
42	Washington	320
43	Montana	289
44	Puerto Rico	252
45	West Virginia	241
46	Utah	180
47	Oregon	157
48	Hawaii	151
49	Wyoming	133
50	Maine	108
51	Vermont	92
52	Alaska	90

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	North Dakota	12
2	Arkansas	6
3	Tennessee	5
4	Wisconsin	5
5	Wyoming	5
6	Iowa	4
7	Montana	4
8	Indiana	3
9	Louisiana	3
10	Mississippi	3
11	Missouri	3
12	Nebraska	3
13	Rhode Island	3
14	South Carolina	3
15	South Dakota	3
16	Delaware	2
17	Idaho	2
18	Illinois	2
19	Massachusetts	2
20	Michigan	2
21	Minnesota	2
22	New Mexico	2
23	North Carolina	2
24	Oklahoma	2
25	Alabama	1
26	California	1
27	Colorado	1
28	Connecticut	1
29	Florida	1
30	Georgia	1
31	Kentucky	1
32	Nevada	1
33	Pennsylvania	1
34	Puerto Rico	1
35	Texas	1
36	Utah	1
37	Washington	1
38	West Virginia	1
39	Alaska	0
40	Arizona	0
41	District of Columbia	0
42	Hawaii	0
43	Kansas	0
44	Maine	0
45	Maryland	0
46	New Hampshire	0
47	New Jersey	0
48	New York	0
49	Ohio	0
50	Oregon	0
51	Vermont	0
52	Virginia	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Lincoln	Arkansas	177,749	1	99
Chattahoochee	Georgia	162,189	2	99
Trousdale	Tennessee	157,480	3	99
Lafayette	Florida	151,389	4	99
Lake	Tennessee	140,821	5	99
Davidson	Tennessee	48,919	279	91
Richland	South Carolina	41,327	483	84
York	South Carolina	23,742	1522	51
Orange	California	19,157	1939	38
Pierce	Washington	12,359	2500	20

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Jerauld	South Dakota	5,961	1	99
Emporia city	Virginia	5,238	2	99
Hancock	Georgia	5,203	3	99
Kenedy	Texas	4,950	4	99
Galax city	Virginia	4,727	5	99
Richland	South Carolina	647	858	72
Davidson	Tennessee	491	1182	62
Orange	California	458	1257	59
York	South Carolina	367	1519	51
Pierce	Washington	275	1818	42

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.